#include <opencv2\video\tracking.hpp>
#include <opencv2\highgui\highgui.hpp>
#include <stdio.h>

using namespace cv;

static inline Point calcPoint(Point2f center, double R, double angle)
{
    return center + Point2f((float)cos(angle), (float)-sin(angle)) * (float)R;
}

static void help()
{
    printf("\nExample of c calls to OpenCV's Kalman filter.\n"
           "   Tracking of rotating point.\n"
           "   Rotation speed is constant.\n"
           "   Both state and measurements vectors are 1D (a point angle),\n"
           "   Measurement is the real point angle + gaussian noise.\n"
           "   The real and the estimated points are connected with yellow line segment,\n"
           "   the real and the measured points are connected with red line segment.\n"
           "   (if Kalman filter works correctly,\n"
           "    the yellow segment should be shorter than the red one).\n"
           "\n"
           "   Pressing any key (except ESC) will reset the tracking with a different speed.\n"
           "   Pressing ESC will stop the program.\n");
}

int main(int, char **)
{
    help();
    Mat img(500, 500, CV_8UC3);
    KalmanFilter KF(2, 1, 0);
    Mat state(2, 1, CV_32F); /* (phi, delta_phi) */
    Mat processNoise(2, 1, CV_32F);
    Mat measurement = Mat::zeros(1, 1, CV_32F);
    char code = (char)-1;

    for (;;)
    {
        randn(state, Scalar::all(0), Scalar::all(0.1));
        KF.transitionMatrix = (Mat_<float>(2, 2) << 1, 1, 0, 1);

        setIdentity(KF.measurementMatrix);
        setIdentity(KF.processNoiseCov, Scalar::all(1e-5));
        setIdentity(KF.measurementNoiseCov, Scalar::all(1e-1));
        setIdentity(KF.errorCovPost, Scalar::all(1));

        randn(KF.statePost, Scalar::all(0), Scalar::all(0.1));

        for (;;)
        {
            Point2f center(img.cols * 0.5f, img.rows * 0.5f);
            float R = img.cols / 3.f;
            double stateAngle = state.at<float>(0);
            Point statePt = calcPoint(center, R, stateAngle);

            Mat prediction = KF.predict();
            double predictAngle = prediction.at<float>(0);
            Point predictPt = calcPoint(center, R, predictAngle);

            randn(measurement, Scalar::all(0), Scalar::all(KF.measurementNoiseCov.at<float>(0)));

            // generate measurement
            measurement += KF.measurementMatrix * state;

            double measAngle = measurement.at<float>(0);
            Point measPt = calcPoint(center, R, measAngle);

            // plot points
#define drawCross(center, color, d)                              \
    line(img, Point(center.x - d, center.y - d),                 \
         Point(center.x + d, center.y + d), color, 1, LINE_AA, 0); \
    line(img, Point(center.x + d, center.y - d),                 \
         Point(center.x - d, center.y + d), color, 1, LINE_AA, 0)

            img = Scalar::all(0);
            drawCross(statePt, Scalar(255, 255, 255), 3);
            drawCross(measPt, Scalar(0, 0, 255), 3);
            drawCross(predictPt, Scalar(0, 255, 0), 3);
            line(img, statePt, measPt, Scalar(0, 0, 255), 3, LINE_AA, 0);
            line(img, statePt, predictPt, Scalar(0, 255, 255), 3, LINE_AA, 0);

            if (theRNG().uniform(0, 4) != 0)
                KF.correct(measurement);

            randn(processNoise, Scalar(0), Scalar::all(sqrt(KF.processNoiseCov.at<float>(0, 0))));
            state = KF.transitionMatrix * state + processNoise;

            imshow("Kalman", img);
            code = (char)waitKey(100);

            if (code > 0)
                break;
        }
        if (code == 27 || code == 'q' || code == 'Q')
            break;
    }

    return 0;
}